Abstract: The purposes of this research were to study the citizen
participation in preventing illegal drugs in one of a poor and small
community of Bangkok, Thailand and to compare the level of
participation and concern of illegal drugs problem by using
demographic variables. This paper drew upon data collected from a
local citizens survey conducted in Bangkok, Thailand during summer
of 2012. A total of 200 respondents were elicited as data input for,
and one way ANOVA test. The findings revealed that the overall
citizen participation was in the level of medium. The mean score
showed that benefit from the program was ranked as the highest and
the decision to participate was ranked as second while the follow-up
of the program was ranked as the lowest.
In terms of the difference in demographic such as gender, age,
level of education, income, and year of residency, the hypothesis
testing’s result disclosed that there were no difference in their level
of participation. However, difference in occupation showed a
difference in their level of participation and concern which was
significant at the 0.05 confidence level.
Abstract: Permeability reduction induced by asphaltene
precipitation during gas injection is one of the serious problems in
the oil industry. This problem can lead to formation damage and
decrease the oil production rate. In this work, Malaysian light oil
sample has been used to investigate the effect CO2 injection and
Water Alternating Gas (WAG) injection on permeability reduction.
In this work, dynamic core flooding experiments were conducted to
study the effect of CO2 and WAG injection on the amount of
asphaltene precipitated. Core properties after displacement were
inspected for any permeability reduction to study the effect of
asphaltene precipitation on rock properties.
The results showed that WAG injection gave less asphaltene
precipitation and formation damage compared to CO2 injection. The
study suggested that WAG injection can be one of the important
factors of managing asphaltene precipitation.
Abstract: Recent research on seeds of bio-diesel plants like
Jatropha curcas, constituting 40-50% bio-crude oil indicates its
potential as one of the most promising alternatives to conventional
sources of energy. Also, limited studies on utilization of de-oiled cake
have revealed that Jatropha bio-waste has good potential to be used as
organic fertilizers produced via aerobic and anaerobic treatment.
However, their commercial exploitation has not yet been possible. The
present study aims at developing appropriate bio-processes and
formulations utilizing Jatropha seed cake as organic fertilizer, for
improving the growth of Polianthes tuberose L. (Tuberose). Pot
experiments were carried out by growing tuberose plants on soil
treated with composted formulations of Jatropha de-oiled cake, Farm
Yard Manure (FYM) and inorganic fertilizers were also blended in
soil. The treatment was carried out through soil amendment as well as
foliar spray. The growth and morphological parameters were
monitored for entire crop cycle.
The growth Length and number of leaves, spike length, rachis
length, number of bulb per plant and earliness of sprouting of bulb and
yield enhancement were comparable to that achieved under inorganic
fertilizer. Furthermore, performance of inorganic fertilizer also showed
an improvement when blended with composted bio-waste. These
findings would open new avenues for Jatropha based bio-wastes to be
composted and used as organic fertilizers for commercial floriculture.
Abstract: Cosmic showers, from their places of origin in space,
after entering earth generate secondary particles called Extensive Air
Shower (EAS). Detection and analysis of EAS and similar High
Energy Particle Showers involve a plethora of experimental setups
with certain constraints for which soft-computational tools like
Artificial Neural Network (ANN)s can be adopted. The optimality
of ANN classifiers can be enhanced further by the use of Multiple
Classifier System (MCS) and certain data - dimension reduction
techniques. This work describes the performance of certain data
dimension reduction techniques like Principal Component Analysis
(PCA), Independent Component Analysis (ICA) and Self Organizing
Map (SOM) approximators for application with an MCS formed
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN). The data inputs are
obtained from an array of detectors placed in a circular arrangement
resembling a practical detector grid which have a higher dimension
and greater correlation among themselves. The PCA, ICA and SOM
blocks reduce the correlation and generate a form suitable for real
time practical applications for prediction of primary energy and
location of EAS from density values captured using detectors in a
circular grid.
Abstract: Nowadays, ontologies are the only widely accepted paradigm for the management of sharable and reusable knowledge in a way that allows its automatic interpretation. They are collaboratively created across the Web and used to index, search and annotate documents. The vast majority of the ontology based approaches, however, focus on indexing texts at document level. Recently, with the advances in ontological engineering, it became clear that information indexing can largely benefit from the use of general purpose ontologies which aid the indexing of documents at word level. This paper presents a concept indexing algorithm, which adds ontology information to words and phrases and allows full text to be searched, browsed and analyzed at different levels of abstraction. This algorithm uses a general purpose ontology, OntoRo, and an ontologically tagged corpus, OntoCorp, both developed for the purpose of this research. OntoRo and OntoCorp are used in a two-stage supervised machine learning process aimed at generating ontology tagging rules. The first experimental tests show a tagging accuracy of 78.91% which is encouraging in terms of the further improvement of the algorithm.
Abstract: The fixed partial dentures are mainly used in the frontal
part of the dental arch because of their great esthetics. There are
several factors that are associated with the stress state created in
ceramic restorations, including: thickness of ceramic layers,
mechanical properties of the materials, elastic modulus of the
supporting substrate material, direction, magnitude and frequency of
applied load, size and location of occlusal contact areas, residual
stresses induced by processing or pores, restoration-cement
interfacial defects and environmental defects. The purpose of this
study is to evaluate the capability of Polarization Sensitive Optical
Coherence Tomography (PSOCT) in detection and analysis of
possible material defects in metal-ceramic and integral ceramic fixed
partial dentures. As a conclusion, it is important to have a non
invasive method to investigate fixed partial prostheses before their
insertion in the oral cavity in order to satisfy the high stress
requirements and the esthetic function.
Abstract: This paper reports on the enhanced photoluminescence
(PL) of nanocomposites through the layered structuring of phosphor
and quantum dot (QD). Green phosphor of Sr2SiO4:Eu, red QDs of
CdSe/CdS/CdZnS/ZnS core-multishell, and thermo-curable resin
were used for this study. Two kinds of composite (layered and mixed)
were prepared, and the schemes for optical energy transfer between
QD and phosphor were suggested and investigated based on PL decay
characteristics. It was found that the layered structure is more effective
than the mixed one in the respects of PL intensity, PL decay and
thermal loss. When this layered nanocomposite (QDs on phosphor) is
used to make white light emitting diode (LED), the brightness is
increased by 37 %, and the color rendering index (CRI) value is raised
to 88.4 compared to the mixed case of 80.4.
Abstract: Railway Stations are prone to emergency due to
various reasons and proper monitor of railway stations are of
immense importance from various angles. A Petri-net representation
of a web-service-based Emergency management system has been
proposed in this paper which will help in monitoring situation of
train, track, signal etc. and in case of any emergency, necessary
resources can be dispatched.
Abstract: One of the robust fault detection filter (RFDF)
designing method is based on sliding-mode theory. The main purpose
of our study is to introduce an innovative simplified reference
residual model generator to formulate the RFDF as a sliding-mode
observer without any manipulation package or transformation matrix,
through which the generated residual signals can be evaluated. So the
proposed design is more explicit and requires less design parameters
in comparison with approaches requiring changing coordinates. To
the best author's knowledge, this is the first time that the sliding
mode technique is applied to detect actuator and sensor faults in a
real boiler. The designing procedure is proposed in a drum boiler in
Synvendska Kraft AB Plant in Malmo, Sweden as a multivariable
and strongly coupled system. It is demonstrated that both sensor and
actuator faults can robustly be detected. Also sensor faults can be
diagnosed and isolated through this method.
Abstract: In recent years, fast neural networks for object/face detection have been introduced based on cross correlation in the frequency domain between the input matrix and the hidden weights of neural networks. In our previous papers [3,4], fast neural networks for certain code detection was introduced. It was proved in [10] that for fast neural networks to give the same correct results as conventional neural networks, both the weights of neural networks and the input matrix must be symmetric. This condition made those fast neural networks slower than conventional neural networks. Another symmetric form for the input matrix was introduced in [1-9] to speed up the operation of these fast neural networks. Here, corrections for the cross correlation equations (given in [13,15,16]) to compensate for the symmetry condition are presented. After these corrections, it is proved mathematically that the number of computation steps required for fast neural networks is less than that needed by classical neural networks. Furthermore, there is no need for converting the input data into symmetric form. Moreover, such new idea is applied to increase the speed of neural networks in case of processing complex values. Simulation results after these corrections using MATLAB confirm the theoretical computations.
Abstract: In this paper, we present a maintenance model of a
two-unit series system with economic dependence. Unit#1 which is
considered to be more expensive and more important, is subject to
condition monitoring (CM) at equidistant, discrete time epochs and
unit#2, which is not subject to CM has a general lifetime distribution.
The multivariate observation vectors obtained through condition
monitoring carry partial information about the hidden state of unit#1,
which can be in a healthy or a warning state while operating. Only the
failure state is assumed to be observable for both units. The objective
is to find an optimal opportunistic maintenance policy minimizing
the long-run expected average cost per unit time. The problem
is formulated and solved in the partially observable semi-Markov
decision process framework. An effective computational algorithm
for finding the optimal policy and the minimum average cost is
developed, illustrated by a numerical example.
Abstract: Mobile ad-hoc networks (MANETs) are a form of
wireless networks which do not require a base station for providing
network connectivity. Mobile ad-hoc networks have many
characteristics which distinguish them from other wireless networks
which make routing in such networks a challenging task. Cluster
based routing is one of the routing schemes for MANETs in which
various clusters of mobile nodes are formed with each cluster having
its own clusterhead which is responsible for routing among clusters.
In this paper we have proposed and implemented a distributed
weighted clustering algorithm for MANETs. This approach is based
on combined weight metric that takes into account several system
parameters like the node degree, transmission range, energy and
mobility of the nodes. We have evaluated the performance of
proposed scheme through simulation in various network situations.
Simulation results show that proposed scheme outperforms the
original distributed weighted clustering algorithm (DWCA).
Abstract: Mankind has entered into an extremely complex and
controversial stage of its development: the world is simultaneously
organized and chaoticized, globalized and localized, combined and
split. Analysts point out that globalization as a process of
strengthening economic, cultural, financial and other ties of states
cause many problems. In the economic sphere, it creates the danger
of growing gap between the states, in the sphere of politics it leads to
the weakening of political power and influence of nation-states.
Abstract: In order to maximize efficiency of an information management platform and to assist in decision making, the collection, storage and analysis of performance-relevant data has become of fundamental importance. This paper addresses the merits and drawbacks provided by the OLAP paradigm for efficiently navigating large volumes of performance measurement data hierarchically. The system managers or database administrators navigate through adequately (re)structured measurement data aiming to detect performance bottlenecks, identify causes for performance problems or assessing the impact of configuration changes on the system and its representative metrics. Of particular importance is finding the root cause of an imminent problem, threatening availability and performance of an information system. Leveraging OLAP techniques, in contrast to traditional static reporting, this is supposed to be accomplished within moderate amount of time and little processing complexity. It is shown how OLAP techniques can help improve understandability and manageability of measurement data and, hence, improve the whole Performance Analysis process.
Abstract: Currently is characterized production engineering
together with the integration of industrial automation and robotics
such very quick view of to manufacture the products. The production
range is continuously changing, expanding and producers have to be
flexible in this regard. It means that need to offer production
possibilities, which can respond to the quick change. Engineering
product development is focused on supporting CAD software, such
systems are mainly used for product design. That manufacturers are
competitive, it should be kept procured machines made available
capable of responding to output flexibility. In response to that
problem is the development of flexible manufacturing systems,
consisting of various automated systems. The integration of flexible
manufacturing systems and subunits together with product design and
of engineering is a possible solution for this issue. Integration is
possible through the implementation of CIM systems. Such a solution
and finding a hyphen between CAD and procurement system ICIM
3000 from Festo Co. is engaged in the research project and this
contribution. This can be designed the products in CAD systems and
watch the manufacturing process from order to shipping by the
development of methods and processes of integration, This can be
modeled in CAD systems products and watch the manufacturing
process from order to shipping to develop methods and processes of
integration, which will improve support for product design
parameters by monitoring of the production process, by creating of
programs for production using the CAD and therefore accelerates the
a total of process from design to implementation.
Abstract: This paper presents the doping profile measurement
and characterization technique for the pocket implanted nano scale
n-MOSFET. Scanning capacitance microscopy and atomic force
microscopy have been used to image the extent of lateral dopant
diffusion in MOS structures. The data are capacitance vs. voltage
measurements made on a nano scale device. The technique is nondestructive
when imaging uncleaved samples. Experimental data from
the published literature are presented here on actual, cleaved device
structures which clearly indicate the two-dimensional dopant profile
in terms of a spatially varying modulated capacitance signal. Firstorder
deconvolution indicates the technique has much promise for
the quantitative characterization of lateral dopant profiles. The pocket
profile is modeled assuming the linear pocket profiles at the source
and drain edges. From the model, the effective doping concentration
is found to use in modeling and simulation results of the various
parameters of the pocket implanted nano scale n-MOSFET. The
potential of the technique to characterize important device related
phenomena on a local scale is also discussed.
Abstract: Today, money laundering (ML) poses a serious threat
not only to financial institutions but also to the nation. This criminal
activity is becoming more and more sophisticated and seems to have
moved from the cliché of drug trafficking to financing terrorism and
surely not forgetting personal gain. Most international financial
institutions have been implementing anti-money laundering solutions
(AML) to fight investment fraud. However, traditional investigative
techniques consume numerous man-hours. Recently, data mining
approaches have been developed and are considered as well-suited
techniques for detecting ML activities. Within the scope of a
collaboration project for the purpose of developing a new solution for
the AML Units in an international investment bank, we proposed a
data mining-based solution for AML. In this paper, we present a
heuristics approach to improve the performance for this solution. We
also show some preliminary results associated with this method on
analysing transaction datasets.
Abstract: This paper presents a new technique for detection of
human faces within color images. The approach relies on image
segmentation based on skin color, features extracted from the two-dimensional
discrete cosine transform (DCT), and self-organizing
maps (SOM). After candidate skin regions are extracted, feature
vectors are constructed using DCT coefficients computed from those
regions. A supervised SOM training session is used to cluster feature
vectors into groups, and to assign “face" or “non-face" labels to those
clusters. Evaluation was performed using a new image database of
286 images, containing 1027 faces. After training, our detection
technique achieved a detection rate of 77.94% during subsequent
tests, with a false positive rate of 5.14%. To our knowledge, the
proposed technique is the first to combine DCT-based feature
extraction with a SOM for detecting human faces within color
images. It is also one of a few attempts to combine a feature-invariant
approach, such as color-based skin segmentation, together with
appearance-based face detection. The main advantage of the new
technique is its low computational requirements, in terms of both
processing speed and memory utilization.
Abstract: To understand working features of a micro combustor,
a computer code has been developed to study combustion of
hydrogen–air mixture in a series of chambers with same shape aspect
ratio but various dimensions from millimeter to micrometer level.
The prepared algorithm and the computer code are capable of
modeling mixture effects in different fluid flows including chemical
reactions, viscous and mass diffusion effects. The effect of various
heat transfer conditions at chamber wall, e.g. adiabatic wall, with
heat loss and heat conduction within the wall, on the combustion is
analyzed. These thermal conditions have strong effects on the
combustion especially when the chamber dimension goes smaller and
the ratio of surface area to volume becomes larger.
Both factors, such as larger heat loss through the chamber wall
and smaller chamber dimension size, may lead to the thermal
quenching of micro-scale combustion. Through such systematic
numerical analysis, a proper operation space for the micro-combustor
is suggested, which may be used as the guideline for microcombustor
design. In addition, the results reported in this paper
illustrate that the numerical simulation can be one of the most
powerful and beneficial tools for the micro-combustor design,
optimization and performance analysis.
Abstract: This is a comprehensive large-sample study of Australian earnings management. Using a sample of 4,844 firm-year observations across nine Australia industries from 2000 to 2006, we find substantial corporate earnings management activity across several Australian industries. We document strong evidence of size and return on assets being primary determinants of earnings management in Australia. The effects of size and return on assets are also found to be dominant in both income-increasing and incomedecreasing earnings manipulation. We also document that that periphery sector firms are more likely to involve larger magnitude of earnings management than firms in the core sector.